Where Did the Data Come From?

About Our Data

By Heather Krause, Chief Data Scientist

At Orb Media, we strive to present stories based on diverse voices from all over the world. We combine emerging data collection techniques with robust statistical analysis. We protect the data of our respondents and are transparent about our methods.

Through our Listen & Learn toolkit, Orb Media collects a wide range of existing datasets and supplement these with new, original data collected using our proprietary tools. We apply innovative computational techniques and empirical strategies to analyze data and generate original research to find new insights about the world and how we collectively fare.

In this story on aging and society, we asked tens of thousands of people from around the world about their experiences and opinions about aging and the elderly.

Sources For This Story

For this project, we combined data from multiple sources and conducted several rounds of statistical analysis using post-stratification survey weights. Weighing and post-stratification are used to take the results of a survey and make them more accurately represent the opinions of a population.

The European Social Survey (2016, collected using randomized stratified sampling methodology)

Our own proprietary survey (2018, collected as a convenience sample using online and in-person collection tools)

Each survey asked respondents to assess the respect of their society towards the elderly. Respondents answered by rating the level of respect on a scale of 1 (very low) to 5 (very high). All surveys also collected basic socio-demographic information, such as respondents’ age.

In combination with the three existing data sets and Orb Survey asked the same question through a multitude of channels and publishing partners. The Orb Media team assessed the homogeneity of key variable distribution by looking at the Pearson Correlation Coefficient (PCC or bivariate correlation) and the intraclass correlation coefficient (ICC). In the PCC, each variable is centered and scaled by its own mean and standard deviation, whereas in the ICC, data are centered and scaled using a pooled mean and standard deviation. The ICC of the key attitude variable between datasets in this study is 0.813, which indicates a stable foundation for combining the data and using the single large dataset for original analysis.

In total Orb Media’s original analysis includes respondents from 101 countries, with a total respondent count of 150,428 people. We have chosen not to report the level of respect towards elderly reported from countries with less than 50 respondents.

What We Discovered

Once we had combined the data at an individual level, we developed a set of post-stratification weights for each row of data. We used this to ensure data was reflective of reality on the ground and not simply the survey respondents.

Using a scale of 1 (very low respect) to 5 (very high respect), the overall average global attitude is 3.75. Averages in individual countries range from 2.75 to 4.8. The standard deviation is 0.57, or about half a point.

Level of Respect for Elderly by Country

Sources: World Values Survey, the European Social Survey and Orb Proprietary Survey

The most significant socio-demographic trend we discovered was the connection between a person’s age and how they feel about the elderly. Interestingly, the youngest and oldest respondents felt the most positive towards elderly people, and the middle age group felt the most negative.

Respect for the Elderly by Age

Sources: World Values Survey, the European Social Survey and Orb Proprietary Survey

After Orb Media had established the level of respect towards elderly in different countries, we looked for data that could tell us something about the possible impact. We compared each country’s average respect rating to other country-level data collected from the United Nations, World Bank, HelpAge, the International Labor Organization, the Organization for Economic Cooperation and Development, and the Eurostat open data collections. We used multilevel mixed-effects modelling to quantify these relationships. This model takes into account several social, economic and geographic attributes of each country such as percent of the population who is elderly, wealth of the country and region of the country.

Our study revealed no meaningful connection between the average gross domestic product or gross national income of a country and the level of respect reported by that country’s respondents. This is an indication that respect for the elderly is not limited to specific economic statuses.

We did find three significant relationships at the country level. A country’s attitude towards aging is significantly related to:

The country’s level of poverty among the elderly compared to the level of general poverty;

The country’s average perceived mental health among the elderly;

The country’s average perceived physical health among the elderly.

Economic Well-Being

A one-point increase in attitude (on the five-point scale) is correlated with a 3 to 5 percentage point decrease in the percent of elderly people living in poverty compared to other age groups in the same country. This includes controlling for country wealth.

In this story, the relative poverty rate is the percentage of people whose income falls below the poverty line (taken as half the median household income of the total population) in a given age group. It is available by broad age group: child poverty (0-17 year old), working-age poverty (18-59 years old), and elderly poverty (60+ years old).

It’s important to note that two countries with the same poverty rates may differ in terms of the relative income level of the poor. (Sources: Poverty Rate, OECD (2015); OECD Stat data on Social Protection and Well-Being, Income Distribution and Poverty, Poverty Rate After Taxes and Transfers, Age Group 66-75, 76+; The Atlas of Social Protection: Indicators of Resilience and Equity, World Bank; Eurostat, At-risk-of-poverty rate by poverty threshold, age and sex (SILC [ilc_li02])c,d)

Respect and Relative Poverty

Sources: OECD 2015, World Bank, Atlas of Social Protection and Eurostat

Physical Health

A one-point increase in the level of respect towards the elderly in a country is related to a 3 to 5 percentage point increase in the number of elderly people in that country who perceive themselves to be in good or very good health.

Self-perceived physical health is measured by asking the respondent to rate their own physical health on a five-point scale, from very bad to very good. (Source: OECD, 2005 – 2015 a. Perceived health status of 65+ OECD plus a few extra 2005-2015)

Respect and Physical Health

Sources: OECD 2005-2015

Mental Health

A one-point increase in the level of respect towards the elderly is related to a 2.5 to 5.5 percentage point increase in the proportion of the elderly population who say they are in good mental health relative to the overall population.

Relative mental health is measured here as the percentage of people over 50 who feel their life has meaning compared with people aged 35-49 who feel the same. (Source: Gallup WorldView, Accessed: 10 April 2013 Year: 2011 or latest available)